Using world-leading, innovative data visualisation tools, the team at DSCallards can assist you and your business translate your data into easily-read charts, graphs and other visually appealing, interactive visuals. Having a visual representation of your data makes it easier for your team to identify and share accurate, real-time patterns. Essentially, data visualisation can provide fresh insight into the trends affecting your business. As a result, the process can predict trends and directly shape your long term strategy.
As the amount of big data circulating grows, more and more businesses are turning to data visualisation in the form of an interactive dashboard to analyse insights and patterns, predict future trends and make strategic, data-driven decisions. Unlike an infographic, which presents a static graphical representation, a dashboard conveys real-time information by pulling complex data points directly from large data sets.
Data visualisation is important because it quite literally paints a picture of what the data means, giving visual context through graphs, maps, and other infographics. As a result, data visualisation is a crucial component of data analytics, as it makes the information easier for the human mind to understand as well as identify trends, patterns, and other outliers.
As the name suggests, data visualisation is the process of finding trends and patterns in data by representing them pictorially. Data is most often visualised using Python through innovative data libraries. By utilising python, we can use various data visualisation tools such as Matplotlib, Seaborn, and Plotly to unearth important business insights.
A good visualisation should be easy to understand as well as accurately present the connections between the data. Connections that are too complex to show with just visuals should be explained with words. Essentially, an audience should be able to quickly understand the data and consider the outcomes.
Quantitative data is based on numbers and is countable or measurable. In contrast to this, qualitative data is interpretive, descriptive, and language-related. Quantitative data tells us how many, how much or how frequently something occurs while qualitative data can assist us in understanding why, how, or what caused specific behaviours, trends, or patterns.
When it comes to data visualisation, quantitative data is often represented as numbers, graphs, and tables. It requires more respondents and utilises closed questions. Qualitative is often presented using words and will feature more open-ended questions.